aiqbench analysis
This paper presents Octopus v3, a compact multimodal AI agent optimized for edge deployment with fewer than one billion parameters. By introducing functional tokens specifically designed to translate visual inputs into actionable outcomes, the model achieves efficient operation on constrained hardware like Raspberry Pi devices while supporting both English and Chinese. The research addresses critical challenges in running sophisticated vision-language models locally without relying solely on cloud infrastructure.
Octopus v3: Technical Report for On-device Sub-billion Multimodal AI Agent
arXiv:2404.11459v3 Announce Type: replace Abstract: A multimodal AI agent is characterized by its ability to process and learn from various types of data, including natural language, visual, and audio inputs, to inform its actions. Despite advancements in large language models that incorporate visual data, such as GPT-4V, effectively translating image-based data into actionable outcomes for AI agents continues to be challenging. In this paper, we introduce a multimodal model that incorporates the concept of functional token specifically designed for AI agent applications. To ensure compatibility with edge devices, our model is optimized to a compact size of less than 1B parameters. Like GPT-4, our model can process both English and Chinese. We demonstrate that this model is capable of operating efficiently on a wide range of edge devices, including as constrained as a Raspberry Pi.
Abstract:A multimodal AI agent is characterized by its ability to process and learn from various types of data, including natural language, visual, and audio inputs, to inform its actions. Despite advancements in large language models that incorporate visual data, such as GPT-4V, effectively translating image-based data into actionable outcomes for AI agents continues to be challenging. In this paper, we introduce a multimodal model that incorporates the concept of functional token specifically designed for AI agent applications. To ensure compatibility with edge devices, our model is optimized to a compact size of less than 1B parameters. Like GPT-4, our model can process both English and Chinese. We demonstrate that this model is capable of operating efficiently on a wide range of edge devices, including as constrained as a Raspberry Pi.
Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2404.11459 [cs.CL]
(or arXiv:2404.11459v3 [cs.CL] for this version)
Full text under the publisher's open licence · 148 words
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